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Spreading of infections on random graphs: A percolation-type model for COVID-19

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  • Croccolo, Fabrizio
  • Roman, H. Eduardo

Abstract

We introduce an epidemic spreading model on a network using concepts from percolation theory. The model is motivated by discussing the standard SIR model, with extensions to describe effects of lockdowns within a population. The underlying ideas and behaviour of the lattice model, implemented using the same lockdown scheme as for the SIR scheme, are discussed in detail and illustrated with extensive simulations. A comparison between both models is presented for the case of COVID-19 data from the USA. Both fits to the empirical data are very good, but some differences emerge between the two approaches which indicate the usefulness of having an alternative approach to the widespread SIR model.

Suggested Citation

  • Croccolo, Fabrizio & Roman, H. Eduardo, 2020. "Spreading of infections on random graphs: A percolation-type model for COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    • Handle: RePEc:eee:chsofr:v:139:y:2020:i:c:s0960077920304744
    DOI: 10.1016/j.chaos.2020.110077
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    References listed on IDEAS

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    1. Fernández-Villaverde, Jesús & Jones, Charles I., 2022. "Estimating and simulating a SIRD Model of COVID-19 for many countries, states, and cities," Journal of Economic Dynamics and Control, Elsevier, vol. 140(C).
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    Cited by:

    1. Yiannis Contoyiannis & Stavros G. Stavrinides & Michael P. Hanias & Myron Kampitakis & Pericles Papadopoulos & Rodrigo Picos & Stelios M. Potirakis, 2020. "A Universal Physics-Based Model Describing COVID-19 Dynamics in Europe," IJERPH, MDPI, vol. 17(18), pages 1-19, September.
    2. Dimou, Argyris & Maragakis, Michael & Argyrakis, Panos, 2022. "A network SIRX model for the spreading of COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
    3. Hector Eduardo Roman & Fabrizio Croccolo, 2021. "Spreading of Infections on Network Models: Percolation Clusters and Random Trees," Mathematics, MDPI, vol. 9(23), pages 1-22, November.
    4. Ronald Manríquez & Camilo Guerrero-Nancuante & Felipe Martínez & Carla Taramasco, 2021. "Spread of Epidemic Disease on Edge-Weighted Graphs from a Database: A Case Study of COVID-19," IJERPH, MDPI, vol. 18(9), pages 1-25, April.
    5. Gandzha, I.S. & Kliushnichenko, O.V. & Lukyanets, S.P., 2021. "Modeling and controlling the spread of epidemic with various social and economic scenarios," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    6. Huang, Binchao & Yang, Jin-Xuan & Li, Xin, 2021. "Identifying influential links to control spreading of epidemics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    7. Matouk, A.E., 2020. "Complex dynamics in susceptible-infected models for COVID-19 with multi-drug resistance," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).

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